title: Enhancing biomedical concept extraction using semantic relationship weights.
abstract: Scientific publications are often associated with a set of keywords to describe their content. Automating the process of keyword extraction and assignment could be useful in indexing electronic documents and building digital libraries. In this paper we propose a new approach to biomedical Concept Extraction (CE) using semantic features of concept graphs. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. We adopt concept relation weights to improve the ranking process of potential key concepts. We perform both objective and human-based subjective evaluations. The results show that using relation weights significantly improves the performance of CE. The results also highlight the subjectivity of the CE procedure as well as of its evaluation.
authors.affiliation: Department of Information Systems, New Jersey Institute of Technology, Newark, NJ 07102, USA. sb252@njit.edu
authors.forename: Said
authors.initials: S
authors.lastname: Bleik
authors.forename: Wei
authors.initials: W
authors.lastname: Xiong
authors.forename: Min
authors.initials: M
authors.lastname: Song
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D035843:Biomedical Research; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics